Tokars Jerome

The Centers for Disease Control and Prevention BioSense project has developed chief complaint (CC) and ICD9 sub-syndrome classifiers for the major syndromes for early event detection and situational awareness. This has the potential to expand the usefulness of syndromic surveillance, but little data exists evaluating this approach. The overall performance of classifiers can differ significantly among syndromes, and presumably among subsyndromes as well. Also, we had previously found that the seasonal pattern of diarrhea was different for patients < 60 months of age (younger) and for patients > 60 months of age (older).

Objective

Using chart review as the criterion standard to estimate the sensitivity, specificity, positive predictive value and negative predictive value of New York State hospital emergency department CC classifiers for patients < 60 months of age and > 60 months of age for the gastrointestinal (GI) syndrome and the following GI sub-syndromes: “abdominal pain”, “nausea-vomiting” and “diarrhea”.

To recognize outbreaks so that early interventions can be applied, BioSense uses a modification of the EARS C2 method, stratifying days used to calculate the expected value by weekend vs weekday, and including a rate-based method that accounts for total visits. These modifications produce lower residuals (observed minus expected counts), but their effect on sensitivity has not been studied.

Objective

To evaluate several variations of a commonlyused control chart method for detecting injected signals in 2 BioSense System datasets.

One of the standard approaches to public health surveillance for influenza is to monitor the percent of visits to about 2000 sentinel physicians for influenza-like illness (%ILI; fever plus cough or sore throat). The BioSense System currently receives (among other data) ICD-9 discharge diagnoses from Veteran’s Affairs (VA) and Department of Defense (DOD) outpatient clinics. A literature review found that, in addition to ICD-9 code 487 (the code specific for influenza), 29 other codes have been used previously to monitor influenza. We evaluated the utility of ICD-9 codes reported to BioSense for their utility in monitoring influenza.

Objective

To determine the utility of current CDC BioSense data sources in monitoring influenza activity at the national and state levels.

The purposes of this study are to validate a keyword-based text parsing algorithm for identifying fractures and compare radiology results with chief complaint and ICD-9 final diagnoses.

The purposes of this study are to identify and characterize increases in emergency department (ED) visits for falls during the 2007-08 winter season.

Objective To examine sub-syndrome distributions among BioSense emergency department (ED) chief complaint and final diagnosis based data and to observe patterns by hospital system, age, and gender.

CDC’s BioSense system provides near-real time situational awareness for public health monitoring through analysis of electronic health data. Determination of anomalous spatial and temporal disease clusters is a crucial part of the daily disease monitoring task. Spatial approaches depend strongly on having reliable estimated values for counts among the geographic sub-regions. If estimates are poor, algorithms will find irrelevant clusters, and clusters of importance may be missed. While many studies have focused on improved computation time and more general cluster shapes, our effort focused on finding anomalies that are correct according to available BioSense data history.

Objective

We applied spatial scan statistics to data from CDC’s BioSense system and examined the effect of the spatial prediction method on determination of anomalous disease clusters. The objectives were to decide on a reliable spatial estimation method for one BioSense data source and to establish criteria for making this decision using other sources.

The goal of this project is to create a set of clinical condition categories based on explicit criteria for use in biosurveillance programs. The categories will be defined and keywords and ICD-9-CM diagnosis codes for implementation will be proposed.

Objective

To describe a standard set of criteria for identifying potentially important anomalies and to compare the criteria with several recent public health events.

Objective The objective of this study was to determine which chief complaints and ICD-9-CM coded diagnoses from real-time BioSense hospital data correlate well with data from conventional influenza surveillance systems.